About GenPeach AI

GenPeach AI is a product-driven research lab aiming to redefine how people create and interact through multimodal, emotionally resonant AI.

We are building vertical foundation models specializing in generating hyper-realistic humans in image & video. Our stack involves working with large-scale proprietary datasets, designing novel model architectures, efficiently training them on large GPU clusters and integrating them in the end-user products.

We train and deploy our own large-scale models and ship them into real products. Our team operates at the intersection of research-grade AI and production-grade systems engineering.

About the Role

We are looking for a Member of Technical Staff (MTS) to own and evolve the AI infrastructure layer that powers both research and production systems.

This is a core infrastructure role with high ownership and direct impact on model training, inference performance, and developer velocity.

In this role, you will

• Own the AI execution and infrastructure layer used by research and product teams

• Design and build high-performance Python systems for:

  • scalable model inference

  • training orchestration

  • large-scale data processing

• Partner closely with research and backend engineers to productionize models and expose them via APIs

• Design and operate distributed pipelines and task queues for batch and streaming workloads

• Optimize GPU inference for latency, throughput, and cost efficiency

• Own the MLOps lifecycle, model deployment and versioning; monitoring and alerting

• Build and maintain CI/CD pipelines for services and ML workflows

• Debug and resolve performance bottlenecks across Python, GPUs, networking, and storage

• Contribute to infrastructure design decisions and long-term architecture

Minimum Qualifications

• 5+ years of professional software engineering experience (Python)

• Strong proficiency in Python, including async programming, multiprocessing / concurrency, performance profiling and optimization

• Experience building and operating high-performance, large-scale distributed systems

• Practical understanding of production ML inference, including:

  • latency constraints

  • reliability and fault tolerance

  • cost and scaling trade-offs

• Hands-on experience with Docker, Kubernetes, Infrastructure-as-Code (Terraform or similar)

• Experience operating systems in Linux production environments

Preferred Qualifications

• Experience with distributed execution frameworks (e.g., Ray)

• Experience working with GPU workloads (training or inference)

• Familiarity with model serving architectures and inference optimization

• Experience handling large-scale image or video datasets (100s of TBs to PBs)

• Strong fundamentals in data structures and algorithms

• Exposure to observability stacks (metrics, logs, tracing) for ML systems

• Experience supporting or collaborating closely with ML research teams

What Makes This Role Unique

• Direct ownership over infrastructure supporting foundation models

• Real impact on model quality, latency, and cost

• Tight collaboration between research and production — no silos

• Small, senior team with high trust and low bureaucracy

• Opportunity to shape systems from first principles

Our Culture

• High ownership and accountability

• Strong technical standards

• Direct, low-ego communication

• Bias toward shipping, measuring, and iterating fast
  

Logistics

• Location: Zurich or Warsaw: onsite or hybrid. If you’re elsewhere, we’re open to remote (team/timezone fit considered).

• Competitive salary + meaningful equity (depending on role and level)

• Interview process: quick screen → technical (practical + systems) → team fit/values